Month: August 2018

This version contains a rewritten core-arbiter, core-container-manger and a new set of build tools. There is now no javascript in the code of databox. Most of the core databox components now communicate over the ZestDB protocol, the language-specific libraries have been updated to reflect this change.

The databox user interface has been moved for the core-container-manger into its own component core-ui. This uses a new experimental store based API to access data and API endpoints within the container-manager. This enhances security and enables audit logging by default. It also has the benefit that new user interfaces can be developed in the same way as databox apps.

The platform-app server has also been removed in favour of a databox driver that read manifest from a git hub repository (driver-app-store).

With these changes, the core of databox is much more stable and should be easier or extend and develop on in the future.

Have fun, and as always expect bugs and dragons. Please report issues on the main me-box/databox repository.

A Research Associate post is available in the Systems Research Group at the Cambridge University Computer Laboratory for up to 2 years with the possibility of extension. Appointment to Senior Research Associate will be considered for exceptional candidates. We welcome applications from candidates with experience outside academia.

The Systems Research Group provides a supportive and rigorous environment in which to undertake world-leading research in a wide range of topics in computer systems. The group’s outputs are not limited to publications but often also include spin out companies significant successes include XenSource (acquired by Citrix Systems Inc. for $500M in 2007) and Unikernel Systems (acquired early last year by Docker Inc.).

This post will focus on design, development and systematic evaluation of technology prototypes for traffic management of domestic IoT devices. This will entail using technologies such as eBPF and Linuxkit to gather data to feed a range of machine learning algorithms, as well as consuming the results of those algorithms alongside user inputs to dynamically reconfigure network connectivity as device behaviours evolve.

Successful candidates will hold a Ph.D. in Computer Science, or have equivalent skills and experience through non-academic routes, and must be able to evidence:

Ability to communicate clearly in English, in both written and spoken forms.

Experience of or aptitude for rigorous system measurement and evaluation, including experiment design, data capture, and data analysis. This may have been gained in commercial or industrial settings as well as through production of academic papers.

Other desirable characteristics include:

Evidence of an excellent publication record, commensurate with level of experience is also desirable. Candidates from outside academia may be able to evidence this by providing examples of rigorous technical writing published or distributed through channels other than academic conferences and journals.

To apply online for this vacancy, please click on the ‘Apply’ button below. This will route you to the University’s Web Recruitment System, where you will need to register an account (if you have not already) and log in before completing the online application form.

Please ensure you upload your Curriculum Vitae (CV) and a covering letter. If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application.

Please quote reference NR16300 on your application and in any correspondence about this vacancy.

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